We derive the asymptotic bias from misclassification of the dependent variable in binary choice models. Measurement error is necessarily non-classical in this case, which leads to bias in linear and non-linear models even if only the dependent variable is mismeasured. A Monte Carlo study and an application to food stamp receipt show that the bias formulas are useful to analyze the sensitivity of substantive conclusions, to interpret biased coefficients and imply features of the estimates that are robust to misclassification. Using administrative records linked to survey data as validation data, we examine estimators that are consistent under misclassification. They can improve estimates if their assumptions hold, but can aggravate the problem if the assumptions are invalid. The estimators differ
in their robustness to such violations, which can be improved by incorporating additional information. We propose tests for the presence and nature of misclassification that can help to choose an estimator.
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A METHOD OF CORRECTING FOR MISREPORTING APPLIED TO THE FOOD STAMP PROGRAM
May 2013
Working Paper Number:
CES-13-28
Survey misreporting is known to be pervasive and bias common statistical analyses. In this paper, I first use administrative data on SNAP receipt and amounts linked to American Community Survey data from New York State to show that survey data can misrepresent the program in important ways. For example, more than 1.4 billion dollars received are not reported in New York State alone. 46 percent of dollars received by house- holds with annual income above the poverty line are not reported in the survey data, while only 19 percent are missing below the poverty line. Standard corrections for measurement error cannot remove these biases. I then develop a method to obtain consistent estimates by combining parameter estimates from the linked data with publicly available data. This conditional density method recovers the correct estimates using public use data only, which solves the problem that access to linked administrative data is usually restricted. I examine the degree to which this approach can be used to extrapolate across time and geography, in order to solve the problem that validation data is often based on a convenience sample. I present evidence from within New York State that the extent of heterogeneity is small enough to make extrapolation work well across both time and geography. Extrapolation to the entire U.S. yields substantive differences to survey data and reduces deviations from official aggregates by a factor of 4 to 9 compared to survey aggregates.
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BIAS IN FOOD STAMPS PARTICIPATION ESTIMATES IN THE PRESENCE OF MISREPORTING ERROR
March 2013
Working Paper Number:
CES-13-13
This paper focuses on how survey misreporting of food stamp receipt can bias demographic estimation of program participation. Food stamps is a federally funded program which subsidizes the nutrition of low-income households. In order to improve the reach of this program, studies on how program participation varies by demographic groups have been conducted using census data. Census data are subject to a lot of misreporting error, both underreporting and over-reporting, which can bias the estimates. The impact of misreporting error on estimate bias is examined by calculating food stamp participation rates, misreporting rates, and bias for select household characteristics (covariates).
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Estimation and Inference in Regression Discontinuity Designs with Clustered Sampling
August 2015
Working Paper Number:
carra-2015-06
Regression Discontinuity (RD) designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is not reasonable in many common applications. To relax this assumption, we derive the properties of traditional non-parametric estimators in a setting that incorporates potential clustering at the level of the running variable, and propose an accompanying optimal-MSE bandwidth selection rule. Simulation results demonstrate that falsely assuming data are i.i.d. when selecting the bandwidth may lead to the choice of bandwidths that are too small relative to the optimal-MSE bandwidth. Last, we apply our procedure using person-level microdata that exhibits clustering at the census tract level to analyze the impact of the Low-Income Housing Tax Credit program on neighborhood characteristics and low-income housing supply.
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Within and Across County Variation in SNAP Misreporting: Evidence from Linked ACS and Administrative Records
July 2014
Working Paper Number:
carra-2014-05
This paper examines sub-state spatial and temporal variation in misreporting of participation in the Supplemental Nutrition Assistance Program (SNAP) using several years of the American Community Survey linked to SNAP administrative records from New York (2008-2010) and Texas (2006-2009). I calculate county false-negative (FN) and false-positive (FP) rates for each year of observation and find that, within a given state and year, there is substantial heterogeneity in FN rates across counties. In addition, I find evidence that FN rates (but not FP rates) persist over time within counties. This persistence in FN rates is strongest among more populous counties, suggesting that when noise from sampling variation is not an issue, some counties have consistently high FN rates while others have consistently low FN rates. This finding is important for understanding how misreporting might bias estimates of sub-state SNAP participation rates, changes in those participation rates, and effects of program participation. This presentation was given at the CARRA Seminar, June 27, 2013
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The Work Disincentive Effects of the Disability Insurance Program in the 1990s
February 2006
Working Paper Number:
CES-06-05
In this paper we evaluate the work disincentive effects of the Disability Insurance program during the 1990s. To accomplish this we construct a new large data set with detailed information on DI application and award decisions and use two different econometric evaluation methods. First, we apply a comparison group approach proposed by John Bound to estimate an upper bound for the work disincentive effect of the current DI program. Second, we adopt a Regression-Discontinuity approach that exploits a particular feature of the DI eligibility determination process to provide a credible point estimate of the impact of the DI program on labor supply for an important subset of DI applicants. Our estimates indicate that during the 1990s the labor force participation rate of DI beneficiaries would have been at most 20 percentage points higher had none received benefits. In addition, we find even smaller labor supply responses for the subset of 'marginal' applicants whose disability determination is based on vocational factors.
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Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation
April 2011
Working Paper Number:
CES-11-14
Benefit receipt in major household surveys is often underreported. This misreporting leads to biased estimates of the economic circumstances of disadvantaged populations, program takeup, and the distributional effects of government programs, and other program effects. We use administrative data on Food Stamp Program (FSP) participation matched to American Community Survey (ACS) and Current Population Survey (CPS) household data. We show that nearly thirty-five percent of true recipient households do not report receipt in the ACS and fifty percent do not report receipt in the CPS. Misreporting, both false negatives and false positives, varies with individual characteristics, leading to complicated biases in FSP analyses. We then directly examine the determinants of program receipt using our combined administrative and survey data. The combined data allow us to examine accurate participation using individual characteristics missing in administrative data. Our results differ from conventional estimates using only survey data, as such estimates understate participation by single parents, non-whites, low income households, and other groups. To evaluate the use of Census Bureau imputed ACS and CPS data, we also examine whether our estimates using survey data alone are closer to those using the accurate combined data when imputed survey observations are excluded. Interestingly, excluding the imputed observations leads to worse ACS estimates, but has less effect on the CPS estimates.
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The Effects of Smoking in Young Adulthood on Smoking and Health Later in Life: Evidence Based on the Vietnam Era Draft Lottery
September 2008
Working Paper Number:
CES-08-35
An important, unresolved question for health policymakers and consumers is whether cigarette smoking in young adulthood has significant lasting effects into later adulthood. The Vietnam era draft lottery offers an opportunity to address this question, because it randomly assigned young men to be more likely to experience conditions favoring cigarette consumption, including highly subsidized prices. Using this natural experiment, we find that military service increased the probability of smoking by 35 percentage points as of 1978-80, when men in the relevant cohorts were aged 25-30, but later in adulthood this effect was substantially attenuated and did not lead to large negative health effects.
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Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning
November 2021
Working Paper Number:
CES-21-35
This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. The linked data reveal new evidence that non-sampling errors in household survey data are correlated with respondents' workplace characteristics.
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Interactions, Neighborhood Selection, and Housing Demand
August 2002
Working Paper Number:
CES-02-19
This paper contributes to the growing literature that identifies and measures the impact of social context on individual economic behavior. We develop a model of housing demand with neighborhood e'ects and neighborhood choice. Modelling neighborhood choice is of fundamental importance in estimating and understanding endogenous and exogenous neighborhood effects. That is, to obtain unbiased estimates of neighborhood effects, it is necessary to control for non-random sorting into neighborhoods. Estimation of the model exploits a unique data set of household data that has been augmented with contextual information at two di'erent levels ('scales') of aggregation. One is at the neighborhood cluster level, of about ten neighbors, with the data coming from a special sample of the American Housing Survey. A second level is the census tract to which these dwelling units belong. Tract-level data are available in the Summary Tape Files of the decennial Census data. We merge these two data sets by gaining access to confidential data of the U.S. Bureau of the Census. We overcome some limitations of these data by implementing some significant methodological advances in estimating discrete choice models. Our results for the neighborhood choice model indicate that individuals prefer to live near others like themselves. This can perpetuate income inequality since those with the best opportunities at economic success will cluster together. The results for the housing demand equation are similar to those in our earlier work [Ioannides and Zabel (2000] where we find evidence of significant endogenous and contextual neighborhood effects.
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Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods
January 2017
Working Paper Number:
CES-17-37
We consider the problem of determining the optimal accuracy of public statistics when increased accuracy requires a loss of privacy. To formalize this allocation problem, we use tools from statistics and computer science to model the publication technology used by a public statistical agency. We derive the demand for accurate statistics from first principles to generate interdependent preferences that account for the public-good nature of both data accuracy and privacy loss. We first show data accuracy is inefficiently undersupplied by a private provider. Solving the appropriate social planner's problem produces an implementable publication strategy. We implement the socially optimal publication plan for statistics on income and health status using data from the American Community Survey, National Health Interview Survey, Federal Statistical System Public Opinion Survey and Cornell National Social Survey. Our analysis indicates that welfare losses from providing too much privacy protection and, therefore, too little accuracy can be substantial.
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